scholarly journals Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4166 ◽  
Author(s):  
Elisabeta-Irina Geană ◽  
Corina Teodora Ciucure ◽  
Constantin Apetrei ◽  
Victoria Artem
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Total Variance ◽  
Red Wines ◽  
Linear Discriminant ◽  
Vis Spectroscopy ◽  
Ft Ir ◽  
Wine Authentication ◽  
Analytical Approaches

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Giuseppina P. Parpinello ◽  
Arianna Ricci ◽  
Panagiotis Arapitsas ◽  
Andrea Curioni ◽  
Luigi Moio ◽  
...  
Keyword(s):  
Principal Component ◽  
Rapid Screening ◽  
Support Vector ◽  
Red Wines ◽  
Grape Variety ◽  
Absorption Bands ◽  
Linear Discriminant ◽  
Mir Spectroscopy ◽  
Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

Optimization of Electronic Nose Sensor Array and its Application in the Classification of Vinegar

2010 ◽  
Vol 121-122 ◽  
pp. 27-32 ◽  
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Xuan Zhou
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Gas Sensors ◽  
Sensor Array ◽  
Principal Component ◽  
Optimization Method ◽  
Linear Discriminant ◽  
Gas Sensor Array ◽  
The Given

Five kinds of vinegars were measured by a gas sensor array composed of six TGS gas sensors. The sensor array should be optimized by the minimal Wilks statistic value, then, the four best sensor array used to detect the type of vinegars were formed, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were applied to analyze the data of primary and optimized sensor array. The results indicated that optimization sensor array could be more adaptable to recognize the five kinds of vinegars. Thereby the given optimization method is effective.

scholarly journals Geographical Origin Traceability of Red Wines Based on Chemometric Classification via Organic Acid Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Yong Huang ◽  
Zi-Tao Jiang ◽  
Jin Tan ◽  
Rong Li
Keyword(s):  
Organic Acid ◽  
High Performance ◽  
Principal Component ◽  
Pinot Noir ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Grape Varieties ◽  
Chromatographic Profiles

A preliminary study on the chemometric classification of red wines produced from different grape varieties and geographical origins was performed based on their chromatographic profiles of organic acids. Tartaric, malic, citric, lactic, acetic, and succinic acids in wines were detected via high performance liquid chromatography (HPLC). Employing multivariate statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA), pattern recognition models were built for the classification of the investigated wines regarding the grape varieties and geographical origins. The PCA clearly grouped the wines according to variety, and the LDA further offered 100% classification ability toward geographical identification of the wines and the leave-one-out cross-validated assignments were 100%, 86.7%, and 100% correct for Cabernet Sauvignon, Merlot, and Pinot Noir wines, respectively. The results reveal the potential of using chromatographic profiles of organic acid as the characteristic indices for chemometric classification of red wines.

scholarly journals Evaluation of Geographic Origin of Torrontés Wines by Chemometrics

2013 ◽  
Vol 2 (5) ◽  
pp. 48 ◽  
Author(s):  
Silvana Mariela Azcarate ◽  
Miguel Angel Cantarelli ◽  
Eduardo Jorge Marchevsky ◽  
José Manuel Camiña
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Uv Spectroscopy ◽  
Geographic Origin ◽  
Principal Component ◽  
San Juan ◽  
Electromagnetic Spectrum ◽  
Linear Discriminant ◽  
Rio Negro ◽  
Vis Spectroscopy

<p>This work discusses the determination of the provenance of commercial Torrontés wines from different Argentinean provinces (Mendoza, San Juan, Salta and Rio Negro) by the use of UV-vis spectroscopy and chemometric techniques. In order to find classification models, wines (n = 80) were analyzed using UV-Vis region of the electromagnetic spectrum. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were used to classify Torrontés wines according to their geographical origin. Classification rates obtained were highly satisfactory. The PLS-DA and LDA calibration models showed that 100% of the Mendoza, San Juan, Salta and Rio Negro Torrontés wine samples had been correctly classified. These results demonstrate the potential use of UV spectroscopy with chemometric data analysis as a method to classify Torrontés wines according to their geographical origin, a procedure which requires low-cost equipment and short-time analysis in comparison with other techniques.</p>

Sign in / Sign up

Export Citation Format

Share Document